Relating Track Geometry to Vehicle Performance Using Neural Network Approach

As part of research leading to the development of the performance-based track geometry (PBTG) inspection technology, the Transportation Technology Center (Association of American Railroads, Pueblo, CO) has developed a technique relating track geometry to vehicle performance, real-time. This technique is based on the neural network (NN) approach, an emerging, powerful tool in recognizing complex patterns and non-linear relationships between many inputs and an output, such as the relationship between track geometry and vehicle response. On the basis of this technique, many NNs have been developed from actual vehicle/track interaction test results. For a given vehicle type, the trained NNs directly relate 3-D track geometry and vehicle operating speed to vehicle performance. The effects of other track conditions such as lubrication, rail profile, and track stiffness are indirectly considered on the basis of their statistical distributions from test results. Because the PBTG inspection is intended to optimize the track geometry maintenance, the vehicle types selected for testing to date were those most sensitive to track geometry (in North America, these are the tank car, covered hopper car, and coal gondola car). However, more NNs for other vehicle types can be easily trained on the basis of the NN technique developed using actual vehicle performance and track geometry test results.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544097
• Authors:
  • Li, D
  • Meddah, A
  • Hass, K
  • Kalay, S
• Publication Date: 2006-9

Language

• English

Media Info

• Media Type: Print
• Features: Appendices; Figures; References;
• Pagination: pp 273-281
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
  • Volume: 220
  • Issue Number: F3
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4097
  • EISSN: 2041-3017
  • Serial URL: http://pif.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Inspection; Maintenance of way; Neural networks; Optimization; Railroad cars; Railroad engineering; Real time information
• Uncontrolled Terms: Track geometry
• Subject Areas: Maintenance and Preservation; Railroads;

Filing Info

• Accession Number: 01042287
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 1 2007 8:37AM